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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/3369
Title: Traffic sign recognition using neural network on open CV: toward intelligent vehicle/driver assistance system
Authors: Auranuch Lorsakul
Mahidol University. Faculty of Engineering. Center for Biomedical and Robotics Technology (BART LAB)
Jackrit Suthakorn
Keywords: Traffic sign recognition;Intelligence vehicle;Neural network
Issue Date: 2007
Abstract: Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or prohibit certain actions. Fast real-time and robust automatic traffic sign detection and recognition can support and disburden the driver and significantly increase driving safety and comfort. Automatic recognition of traffic signs is also important for an automated intelligent driving vehicle or for driver assistance systems. This paper presents a study to recognize traffic sign patterns using Neural Network technique. Images are pre-processed with several image processing techniques, such as, threshold techniques, Gaussian filter, Canny edge detection, Contour and Fit Ellipse. Then, the Neural Networks stages are performed to recognize the traffic sign patterns. The system is trained and validated to find the best network architecture. The experimental results show highly accurate classifications of traffic sign patterns with complex background images as well as the results accomplish in reducing the computational cost of this proposed method.
URI: http://repository.li.mahidol.ac.th/dspace/handle/123456789/3369
metadata.dc.identifier.url: http://www.bartlab.org/Dr.%20Jackrit's%20Papers/ney/1.TRAFFIC_SIGN_Lorsakul_ISR.pdf
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